I am an assistant professor at UC San Diego in the ECE department. I am affliated with the CSE department, Center for Visual Computing, Contextual Robotics Institute, and Artificial Intelligence Group. I am a member of the Robotics team in the TILOS NSF AI Institute.
I was a postdoctoral fellow at UC Berkeley with Alexei Efros and Trevor Darrell. I received a Ph.D. in robotics from the Carnegie Mellon University, at where I worked with Abhinav Gupta. Here is my PhD Thesis.
I am hiring a postdoc!
Research Group
Our group has a broad interest around the directions of Computer Vision, Machine Learning and Robotics. Our focus is on learning 3D and dynamics representations through videos and physical robotic interaction data. We explore various means of supervision signals from the data itself, language, and common sense knowledge. We leverage these comprehensive representations to facilitate the learning of robot skills, with the goal of generalizing the robot to interact effectively with a wide range of objects and environments in the real physical world. Please check out our individual research topic of Self-Supervised Learning, Video Understanding, Common Sense Reasoning, RL and Robotics, 3D Interaction, Dexterous Hand.
Teaching
ECE285: Introduction to Visual Learning (Spring 2023).
ECE176: Introduction to Deep Learning & Applications (Winter 2023).
ECE285: Introduction to Visual Learning (Spring 2022).
ECE285: Introduction to Visual Learning (Spring 2021).
ECE176: Introduction to Deep Learning & Applications (Winter 2021).
News
I gave a talk in the MIT Embodied Intelligence Seminar on Geometric Robot Learning for Generalizable Skills Acquisition.
I gave a talk in the Workshop on Neural Fields across Fields in ICLR 2023, on Generalization in Neural Radiance Fields. Also see our recent release on Generalizable NeRFs: TUVF, ActorsNeRF, FeatureNeRF.
I am co-organizing the Workshop on Learning Dexterous Manipulation in RSS 2023.
I am co-organizing the Workshop on 4D Hand Object Interaction in CVPR 2023.
Received the Adobe Data Science Research Award, 2023.
Received the NSF CAREER Award, 2023.
Received the Amazon Research Award, x2, 2022.
Received the Sony Research Award, 2021.
Congratulations to Yinbo Chen and Jiarui Xu on winning the Qualcomm Innovation Fellowship 2022.
I serve as an Area Chair for ICLR 2023, CVPR 2021,2022, ECCV 2022, ICCV 2021, Session Co-Chair for ICRA 2021, IROS 2022, SPC for AAAI 2022, 2023.
I gave a talk on Learning Rich and Generalizable Representation for Visual Motor Control at Nuro, Covariant, UCSD Visual Computing Retreat, Brown Visual Computing Seminar. Here is the recorded video.
I am co-organizing the Tutorial on Building and Working in Environments for Embodied AI in CVPR 2022. Here is the code base for the tutorial.
I gave a talk in the Workshop on Generalizable Policy Learning in the Physical World in ICLR 2022, on Generalizing Dexterous Manipulation by Learning from Humans.
I gave a talk in the Tutorial on Large Scale Holistic Video Understanding in ICCV 2021, on Learning to Perceive Videos for Embodiment.
I gave a talk in the Large-scale Video Object Segmentation Challenge Workshop in CVPR 2021, on Self-Supervised Representation Learning with Videos.
I am co-organizing the 3rd Tutorial on Learning Representations via Graph-structured Networks in CVPR 2021. Here is the recorded video.
I am co-organizing the Comprehensive Tutorial on Video Modeling in CVPR 2021.
I gave a talk in Nvidia on Self-Supervised Learning. Here is the recorded video.
I gave a talk in the 4D Vision Workshop in ECCV 2020. Here is the recorded video.
I am co-organizing the Workshop on Sensing, Understanding and Synthesizing Humans in ECCV 2020.
I am co-organizing the 2nd Tutorial on Learning Representations via Graph-structured Networks in CVPR 2020. Here is the recorded video.
I am co-organizing the Tutorial on Learning Representations via Graph-structured Networks in CVPR 2019.
I am co-organizing the Workshop on Multi-Modal Learning from Videos in CVPR 2019.
I gave a talk in the Tutorial on GANs in CVPR 2018, here is the recorded video.